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<div class="title">unary_classifier.hpp</div>  </div>
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<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">/*</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment"> * Software License Agreement (BSD License)</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment"> *  Point Cloud Library (PCL) - www.pointclouds.org</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment"> *  All rights reserved.</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment"> *  Redistribution and use in source and binary forms, with or without</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160;<span class="comment"> *  modification, are permitted provided that the following conditions</span></div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="comment"> *  are met:</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160;<span class="comment"> *   * Redistributions of source code must retain the above copyright</span></div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="comment"> *     notice, this list of conditions and the following disclaimer.</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160;<span class="comment"> *   * Redistributions in binary form must reproduce the above</span></div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="comment"> *     copyright notice, this list of conditions and the following</span></div>
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<div class="line"><a name="l00018"></a><span class="lineno">   18</span>&#160;<span class="comment"> *   * Neither the name of the copyright holder(s) nor the names of its</span></div>
<div class="line"><a name="l00019"></a><span class="lineno">   19</span>&#160;<span class="comment"> *     contributors may be used to endorse or promote products derived</span></div>
<div class="line"><a name="l00020"></a><span class="lineno">   20</span>&#160;<span class="comment"> *     from this software without specific prior written permission.</span></div>
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<div class="line"><a name="l00022"></a><span class="lineno">   22</span>&#160;<span class="comment"> *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS</span></div>
<div class="line"><a name="l00023"></a><span class="lineno">   23</span>&#160;<span class="comment"> *  &quot;AS IS&quot; AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT</span></div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="comment"> *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS</span></div>
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<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;<span class="comment"> *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER</span></div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;<span class="comment"> *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT</span></div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;<span class="comment"> *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN</span></div>
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<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;<span class="comment"> * Author : Christian Potthast</span></div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;<span class="comment"> * Email  : potthast@usc.edu</span></div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;<span class="comment"> *</span></div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;<span class="comment"> */</span></div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160; </div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;<span class="preprocessor">#ifndef PCL_UNARY_CLASSIFIER_HPP_</span></div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160;<span class="preprocessor">#define PCL_UNARY_CLASSIFIER_HPP_</span></div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160; </div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;<span class="preprocessor">#include &lt;Eigen/Core&gt;</span></div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;<span class="preprocessor">#include &lt;pcl/segmentation/unary_classifier.h&gt;</span></div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;<span class="preprocessor">#include &lt;pcl/common/io.h&gt;</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;<span class="preprocessor">#include &lt;pcl/kdtree/flann.h&gt;</span></div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160; </div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00050"></a><span class="lineno"><a class="line" href="classpcl_1_1_unary_classifier.html#aa17330bb95171dc661078966e77f25d4">   50</a></span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html#aa17330bb95171dc661078966e77f25d4">pcl::UnaryClassifier&lt;PointT&gt;::UnaryClassifier</a> () :</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  input_cloud_ (new pcl::<a class="code" href="classpcl_1_1_point_cloud.html">PointCloud</a>&lt;<a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a>&gt;),</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  label_field_ (false),</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  normal_radius_search_ (0.01f),</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  fpfh_radius_search_ (0.05f),</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;  feature_threshold_ (5.0)</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;{</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;}</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160; </div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt;</div>
<div class="line"><a name="l00061"></a><span class="lineno"><a class="line" href="classpcl_1_1_unary_classifier.html#ac2bf8c9495c565cc2c637161d36ff064">   61</a></span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html#ac2bf8c9495c565cc2c637161d36ff064">pcl::UnaryClassifier&lt;PointT&gt;::~UnaryClassifier</a> ()</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;{</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;}</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160; </div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00067"></a><span class="lineno"><a class="line" href="classpcl_1_1_unary_classifier.html#a25f5b661dacac2c2853a41e43a06cdea">   67</a></span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html#a25f5b661dacac2c2853a41e43a06cdea">pcl::UnaryClassifier&lt;PointT&gt;::setInputCloud</a> (<span class="keyword">typename</span> pcl::PointCloud&lt;PointT&gt;::Ptr input_cloud)</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;{</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <span class="keywordflow">if</span> (input_cloud_ != NULL)</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;    input_cloud_.reset ();</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160; </div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  input_cloud_ = input_cloud;</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160; </div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud &lt;PointT&gt;</a> point;</div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  std::vector&lt;pcl::PCLPointField&gt; fields;</div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160; </div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  <span class="keywordtype">int</span> label_index = -1;</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;  label_index = <a class="code" href="group__common.html#ga2bc4b9a4e25de1d0b00db4e41f0ad682">pcl::getFieldIndex</a> (point, <span class="stringliteral">&quot;label&quot;</span>, fields);</div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;  </div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;  <span class="keywordflow">if</span> (label_index != -1)</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;    label_field_ = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;}</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160; </div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::convertCloud</a> (<span class="keyword">typename</span> pcl::PointCloud&lt;PointT&gt;::Ptr in,</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;                                            pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr out)</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;{</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;  <span class="comment">// resize points of output cloud</span></div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160; </div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;  {</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <a class="code" href="structpcl_1_1_point_x_y_z.html">pcl::PointXYZ</a> point;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;    <span class="comment">// fill X Y Z</span></div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;    point.x = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x;</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;    point.y = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y;</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;    point.z = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z;</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i] = point;</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;  }</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;}</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160; </div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::convertCloud</a> (<span class="keyword">typename</span> pcl::PointCloud&lt;PointT&gt;::Ptr in,</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;                                            pcl::PointCloud&lt;pcl::PointXYZRGBL&gt;::Ptr out)</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;{</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;  <span class="comment">// TODO:: check if input cloud has RGBA information and insert into the cloud</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160; </div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;  <span class="comment">// resize points of output cloud</span></div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160; </div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;  {</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;    <a class="code" href="structpcl_1_1_point_x_y_z_r_g_b_l.html">pcl::PointXYZRGBL</a> point;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;    <span class="comment">// X Y Z R G B L</span></div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    point.x = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x;</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;    point.y = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;    point.z = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;    <span class="comment">//point.rgba = in-&gt;points[i].rgba;</span></div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;    point.label = 1;</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i] = point;</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;  }</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;}</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160; </div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::findClusters</a> (<span class="keyword">typename</span> pcl::PointCloud&lt;PointT&gt;::Ptr in,</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;                                            std::vector&lt;int&gt; &amp;cluster_numbers)</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;{</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160;  <span class="comment">// find the &#39;label&#39; field index</span></div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  std::vector &lt;pcl::PCLPointField&gt; fields;</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;  <span class="keywordtype">int</span> label_idx = -1;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud &lt;PointT&gt;</a> point;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;  label_idx = <a class="code" href="group__common.html#ga2bc4b9a4e25de1d0b00db4e41f0ad682">pcl::getFieldIndex</a> (point, <span class="stringliteral">&quot;label&quot;</span>, fields);</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160; </div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;  <span class="keywordflow">if</span> (label_idx != -1)</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;  {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    {</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      <span class="comment">// get the &#39;label&#39; field                                                                       </span></div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;      uint32_t label;      </div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      memcpy (&amp;label, <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i]) + fields[label_idx].offset, <span class="keyword">sizeof</span>(uint32_t));</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160; </div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;      <span class="comment">// check if label exist</span></div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160;      <span class="keywordtype">bool</span> exist = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; cluster_numbers.size (); j++)</div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;      {</div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;        <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span>uint32_t<span class="keyword">&gt;</span> (cluster_numbers[j]) == label)</div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;        {</div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;          exist = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;          <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;        }</div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;      }</div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;      <span class="keywordflow">if</span> (!exist)</div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;        cluster_numbers.push_back (label);</div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;    }    </div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;  }</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;}</div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160; </div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::getCloudWithLabel</a> (<span class="keyword">typename</span> pcl::PointCloud&lt;PointT&gt;::Ptr in,</div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;                                                 pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr out,</div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;                                                 <span class="keywordtype">int</span> label_num)</div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;{</div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;  <span class="comment">// find the &#39;label&#39; field index</span></div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;  std::vector &lt;pcl::PCLPointField&gt; fields;</div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;  <span class="keywordtype">int</span> label_idx = -1;</div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;  <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud &lt;PointT&gt;</a> point;</div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;  label_idx = <a class="code" href="group__common.html#ga2bc4b9a4e25de1d0b00db4e41f0ad682">pcl::getFieldIndex</a> (point, <span class="stringliteral">&quot;label&quot;</span>, fields);</div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160; </div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;  <span class="keywordflow">if</span> (label_idx != -1)</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;  {</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    {</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;      <span class="comment">// get the &#39;label&#39; field                                                                       </span></div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;      uint32_t label;</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;      memcpy (&amp;label, <span class="keyword">reinterpret_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span> (&amp;in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i]) + fields[label_idx].offset, <span class="keyword">sizeof</span>(uint32_t));</div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160; </div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;      <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (label) == label_num)</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160;      {</div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;        <a class="code" href="structpcl_1_1_point_x_y_z.html">pcl::PointXYZ</a> point;</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;        <span class="comment">// X Y Z</span></div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;        point.x = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].x;</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;        point.y = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].y;</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160;        point.z = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].z;</div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;        out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.push_back (point);</div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;      }</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;    }</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;  }</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;}</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::computeFPFH</a> (pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr in,</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;                                           pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr out,</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;                                           <span class="keywordtype">float</span> normal_radius_search,</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;                                           <span class="keywordtype">float</span> fpfh_radius_search)</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;{</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;  pcl::PointCloud&lt;pcl::Normal&gt;::Ptr normals (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::Normal&gt;</a> ());</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;  pcl::search::KdTree&lt;pcl::PointXYZ&gt;::Ptr normals_tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointXYZ&gt;</a>);</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;  <a class="code" href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation&lt;pcl::PointXYZ, pcl::Normal&gt;</a> n3d;</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160; </div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;  n3d.<a class="code" href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">setRadiusSearch</a> (normal_radius_search);</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  n3d.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (normals_tree);</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;  <span class="comment">// ---[ Estimate the point normals</span></div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;  n3d.<a class="code" href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">setInputCloud</a> (in);</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;  n3d.<a class="code" href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">compute</a> (*normals);</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160; </div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  <span class="comment">// Create the FPFH estimation class, and pass the input dataset+normals to it</span></div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;  <a class="code" href="classpcl_1_1_f_p_f_h_estimation.html">pcl::FPFHEstimation&lt;pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33&gt;</a> fpfh;</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;  fpfh.<a class="code" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (in);</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;  fpfh.<a class="code" href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">setInputNormals</a> (normals);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;  </div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160;  pcl::search::KdTree&lt;pcl::PointXYZ&gt;::Ptr tree (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;pcl::PointXYZ&gt;</a>);</div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  fpfh.<a class="code" href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">setSearchMethod</a> (tree);</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;  fpfh.<a class="code" href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">setRadiusSearch</a> (fpfh_radius_search);</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;  <span class="comment">// Compute the features</span></div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;  fpfh.<a class="code" href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">compute</a> (*out);</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;}</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160; </div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::kmeansClustering</a> (pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr in,</div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;                                                pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr out,</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;                                                <span class="keywordtype">int</span> k)</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;{</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  <a class="code" href="classpcl_1_1_kmeans.html">pcl::Kmeans</a> kmeans (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ()), 33);</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160;  kmeans.setClusterSize (k);</div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160; </div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;  <span class="comment">// add points to the clustering</span></div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;  {</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;    std::vector&lt;float&gt; data (33);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> idx = 0; idx &lt; 33; idx++)</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160;      data[idx] = in-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].histogram[idx];</div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;    kmeans.addDataPoint (data);</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;  }</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160; </div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;  <span class="comment">// k-means clustering</span></div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  kmeans.kMeans ();</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  <span class="comment">// get the cluster centroids</span></div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;  pcl::Kmeans::Centroids centroids = kmeans.get_centroids ();</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160; </div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;  <span class="comment">// initialize output cloud</span></div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">width</a> = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (centroids.size ());</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">height</a> = 1;</div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">is_dense</a> = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;  out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.resize (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (centroids.size ()));</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;  <span class="comment">// copy cluster centroids into feature cloud </span></div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; centroids.size (); i++)</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;  {</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;    <a class="code" href="structpcl_1_1_f_p_f_h_signature33.html">pcl::FPFHSignature33</a> point;</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> idx = 0; idx &lt; 33; idx++)</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160;      point.histogram[idx] = centroids[i][idx];</div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160;    out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i] = point;</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  }</div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;}</div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160; </div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::queryFeatureDistances</a> (std::vector&lt;pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr&gt; &amp;trained_features,</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;                                                     pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr query_features,</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;                                                     std::vector&lt;int&gt; &amp;indi,</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;                                                     std::vector&lt;float&gt; &amp;dist)</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;{</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160;  <span class="comment">// estimate the total number of row&#39;s needed</span></div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;  <span class="keywordtype">int</span> n_row = 0;</div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; trained_features.size (); i++)</div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;    n_row += <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (trained_features[i]-&gt;points.size ());</div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160; </div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;  <span class="comment">// Convert data into FLANN format</span></div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;  <span class="keywordtype">int</span> n_col = 33;</div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;  <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;float&gt;</a> data (<span class="keyword">new</span> <span class="keywordtype">float</span>[n_row * n_col], n_row, n_col);</div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> k = 0; k &lt; trained_features.size (); k++)</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;  {</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160;    pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr hist = trained_features[k];</div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;    <span class="keywordtype">size_t</span> c = hist-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ();</div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; c; ++i)</div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> j = 0; j &lt; data.cols; ++j)</div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;        data[(k * c) + i][j] = hist-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].histogram[j];</div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;  }</div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160; </div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;  <span class="comment">// build kd-tree given the training features</span></div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;  <a class="code" href="classflann_1_1_index.html">flann::Index&lt;flann::ChiSquareDistance&lt;float&gt;</a> &gt; *index;</div>
<div class="line"><a name="l00297"></a><span class="lineno">  297</span>&#160;  index = <span class="keyword">new</span> <a class="code" href="classflann_1_1_index.html">flann::Index&lt;flann::ChiSquareDistance&lt;float&gt;</a> &gt; (data, flann::LinearIndexParams ());</div>
<div class="line"><a name="l00298"></a><span class="lineno">  298</span>&#160;  <span class="comment">//flann::Index&lt;flann::ChiSquareDistance&lt;float&gt; &gt; index (data, flann::LinearIndexParams ());</span></div>
<div class="line"><a name="l00299"></a><span class="lineno">  299</span>&#160;  <span class="comment">//flann::Index&lt;flann::ChiSquareDistance&lt;float&gt; &gt; index (data, flann::KMeansIndexParams (5, -1));  </span></div>
<div class="line"><a name="l00300"></a><span class="lineno">  300</span>&#160;  <span class="comment">//flann::Index&lt;flann::ChiSquareDistance&lt;float&gt; &gt; index (data, flann::KDTreeIndexParams (4));</span></div>
<div class="line"><a name="l00301"></a><span class="lineno">  301</span>&#160;  index-&gt;buildIndex ();</div>
<div class="line"><a name="l00302"></a><span class="lineno">  302</span>&#160; </div>
<div class="line"><a name="l00303"></a><span class="lineno">  303</span>&#160;  <span class="keywordtype">int</span> k = 1;</div>
<div class="line"><a name="l00304"></a><span class="lineno">  304</span>&#160;  indi.resize (query_features-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00305"></a><span class="lineno">  305</span>&#160;  dist.resize (query_features-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size ());</div>
<div class="line"><a name="l00306"></a><span class="lineno">  306</span>&#160;  <span class="comment">// Query all points</span></div>
<div class="line"><a name="l00307"></a><span class="lineno">  307</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; query_features-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00308"></a><span class="lineno">  308</span>&#160;  {</div>
<div class="line"><a name="l00309"></a><span class="lineno">  309</span>&#160;    <span class="comment">// Query point  </span></div>
<div class="line"><a name="l00310"></a><span class="lineno">  310</span>&#160;    <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;float&gt;</a> p = <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;float&gt;</a>(<span class="keyword">new</span> <span class="keywordtype">float</span>[n_col], 1, n_col);</div>
<div class="line"><a name="l00311"></a><span class="lineno">  311</span>&#160;    memcpy (&amp;p.ptr ()[0], query_features-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].histogram, p.cols * p.rows * sizeof (<span class="keywordtype">float</span>));</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160; </div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;    <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;int&gt;</a> indices (<span class="keyword">new</span> <span class="keywordtype">int</span>[k], 1, k);</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;    <a class="code" href="classflann_1_1_matrix.html">flann::Matrix&lt;float&gt;</a> distances (<span class="keyword">new</span> <span class="keywordtype">float</span>[k], 1, k);  </div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;    index-&gt;knnSearch (p, indices, distances, k, flann::SearchParams (512));</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160; </div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    indi[i] = indices[0][0];</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    dist[i] = distances[0][0];</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160; </div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    <span class="keyword">delete</span>[] p.ptr ();</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;  }</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160; </div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  <span class="comment">//std::cout &lt;&lt; &quot;kdtree size: &quot; &lt;&lt; index-&gt;size () &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160; </div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160;  <span class="keyword">delete</span>[] data.ptr ();</div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;}</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160; </div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::assignLabels</a> (std::vector&lt;int&gt; &amp;indi,</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;                                            std::vector&lt;float&gt; &amp;dist,</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;                                            <span class="keywordtype">int</span> n_feature_means,</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;                                            <span class="keywordtype">float</span> feature_threshold,</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;                                            pcl::PointCloud&lt;pcl::PointXYZRGBL&gt;::Ptr out)</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;                              </div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160;{</div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;  <span class="keywordtype">float</span> nfm = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (n_feature_means);</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>.size (); i++)</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;  {</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;    <span class="keywordflow">if</span> (dist[i] &lt; feature_threshold)</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;    {</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160;      <span class="keywordtype">float</span> l = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span> (indi[i]) / nfm;</div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;      <span class="keywordtype">float</span> intpart;</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;      <span class="comment">//float fractpart = modf (l , &amp;intpart);</span></div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160;      std::modf (l , &amp;intpart);</div>
<div class="line"><a name="l00346"></a><span class="lineno">  346</span>&#160;      <span class="keywordtype">int</span> label = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (intpart);</div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;      </div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;      out-&gt;<a class="code" href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">points</a>[i].label = label+2;</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;    }</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160;  }</div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;}</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160; </div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160; </div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::train</a> (pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr &amp;output)</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160;{  </div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="comment">// convert cloud into cloud with XYZ</span></div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;  pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr tmp_cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointXYZ&gt;</a>);</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;  convertCloud (input_cloud_, tmp_cloud);</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160; </div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;  <span class="comment">// compute FPFH feature histograms for all point of the input point cloud</span></div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;  pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr feature (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a>);</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160;  computeFPFH (tmp_cloud, feature, normal_radius_search_, fpfh_radius_search_);</div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160; </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;  <span class="comment">//PCL_INFO (&quot;Number of input cloud features: %d\n&quot;, static_cast&lt;int&gt; (feature-&gt;points.size ()));</span></div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160; </div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;  <span class="comment">// use k-means to cluster the features</span></div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  kmeansClustering (feature, output, cluster_size_);</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;}</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160; </div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::trainWithLabel</a> (</div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;    std::vector&lt;<a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a>, Eigen::aligned_allocator&lt;<a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a> &gt; &gt; &amp;output)</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;{</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  <span class="comment">// find clusters</span></div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  std::vector&lt;int&gt; cluster_numbers;</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  findClusters (input_cloud_, cluster_numbers);</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;  std::cout &lt;&lt; <span class="stringliteral">&quot;cluster numbers: &quot;</span>;</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; cluster_numbers.size (); i++)</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160;    std::cout &lt;&lt; cluster_numbers[i] &lt;&lt; <span class="stringliteral">&quot; &quot;</span>;</div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;  std::cout &lt;&lt; std::endl;</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160; </div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; cluster_numbers.size (); i++)</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;  {    </div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    <span class="comment">// extract all points with the same label number</span></div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr label_cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointXYZ&gt;</a>);</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    getCloudWithLabel (input_cloud_, label_cloud, cluster_numbers[i]);</div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160; </div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;    <span class="comment">// compute FPFH feature histograms for all point of the input point cloud</span></div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160;    pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr feature (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a>);</div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;    computeFPFH (label_cloud, feature, normal_radius_search_, fpfh_radius_search_);</div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160; </div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;    <span class="comment">// use k-means to cluster the features</span></div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160;    pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr kmeans_feature (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a>);</div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;    kmeansClustering (feature, kmeans_feature, cluster_size_);</div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160; </div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;    output.push_back (*kmeans_feature);</div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;  }</div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;}</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160; </div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> Po<span class="keywordtype">int</span>T&gt; <span class="keywordtype">void</span></div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160;<a class="code" href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier&lt;PointT&gt;::segment</a> (pcl::PointCloud&lt;pcl::PointXYZRGBL&gt;::Ptr &amp;out)</div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;{</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;  <span class="keywordflow">if</span> (trained_features_.size () &gt; 0)</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160;  {</div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;    <span class="comment">// convert cloud into cloud with XYZ</span></div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;    pcl::PointCloud&lt;pcl::PointXYZ&gt;::Ptr tmp_cloud (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::PointXYZ&gt;</a>);</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;    convertCloud (input_cloud_, tmp_cloud);</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160; </div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;    <span class="comment">// compute FPFH feature histograms for all point of the input point cloud</span></div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;    pcl::PointCloud&lt;pcl::FPFHSignature33&gt;::Ptr input_cloud_features (<span class="keyword">new</span> <a class="code" href="classpcl_1_1_point_cloud.html">pcl::PointCloud&lt;pcl::FPFHSignature33&gt;</a>);</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160;    computeFPFH (tmp_cloud, input_cloud_features, normal_radius_search_, fpfh_radius_search_);</div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160; </div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;    <span class="comment">// query the distances from the input data features to all trained features</span></div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;    std::vector&lt;int&gt; indices;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160;    std::vector&lt;float&gt; <a class="code" href="common_2include_2pcl_2common_2geometry_8h.html#a2fc89f0c26b7c7377fcd2851fa933b87">distance</a>;</div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;    queryFeatureDistances (trained_features_, input_cloud_features, indices, distance);</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160; </div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;    <span class="comment">// assign a label to each point of the input point cloud</span></div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;    <span class="keywordtype">int</span> n_feature_means = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (trained_features_[0]-&gt;points.size ());</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;    convertCloud (input_cloud_, out);</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;    assignLabels (indices, distance, n_feature_means, feature_threshold_, out);</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;    <span class="comment">//std::cout &lt;&lt; &quot;Assign labels - DONE&quot; &lt;&lt; std::endl;</span></div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;  }</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;  <span class="keywordflow">else</span></div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;    PCL_ERROR (<span class="stringliteral">&quot;no training features set \n&quot;</span>);</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;}</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160; </div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;<span class="preprocessor">#define PCL_INSTANTIATE_UnaryClassifier(T) template class pcl::UnaryClassifier&lt;T&gt;;</span></div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160; </div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160;<span class="preprocessor">#endif    </span><span class="comment">// PCL_UNARY_CLASSIFIER_HPP_</span></div>
<div class="ttc" id="aclassflann_1_1_index_html"><div class="ttname"><a href="classflann_1_1_index.html">flann::Index</a></div><div class="ttdef"><b>Definition:</b> kdtree_flann.h:54</div></div>
<div class="ttc" id="aclassflann_1_1_matrix_html"><div class="ttname"><a href="classflann_1_1_matrix.html">flann::Matrix&lt; float &gt;</a></div></div>
<div class="ttc" id="aclasspcl_1_1_f_p_f_h_estimation_html"><div class="ttname"><a href="classpcl_1_1_f_p_f_h_estimation.html">pcl::FPFHEstimation</a></div><div class="ttdoc">FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud d...</div><div class="ttdef"><b>Definition:</b> fpfh.h:81</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_from_normals_html_a349685ac9deb723502de9f399d0286dc"><div class="ttname"><a href="classpcl_1_1_feature_from_normals.html#a349685ac9deb723502de9f399d0286dc">pcl::FeatureFromNormals::setInputNormals</a></div><div class="ttdeci">void setInputNormals(const PointCloudNConstPtr &amp;normals)</div><div class="ttdoc">Provide a pointer to the input dataset that contains the point normals of the XYZ dataset....</div><div class="ttdef"><b>Definition:</b> feature.h:344</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_a44829319486a2dc415a4e068dc55c577"><div class="ttname"><a href="classpcl_1_1_feature.html#a44829319486a2dc415a4e068dc55c577">pcl::Feature::setRadiusSearch</a></div><div class="ttdeci">void setRadiusSearch(double radius)</div><div class="ttdoc">Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...</div><div class="ttdef"><b>Definition:</b> feature.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ace1caca622f06eee8ad1911228324792"><div class="ttname"><a href="classpcl_1_1_feature.html#ace1caca622f06eee8ad1911228324792">pcl::Feature::setSearchMethod</a></div><div class="ttdeci">void setSearchMethod(const KdTreePtr &amp;tree)</div><div class="ttdoc">Provide a pointer to the search object.</div><div class="ttdef"><b>Definition:</b> feature.h:166</div></div>
<div class="ttc" id="aclasspcl_1_1_feature_html_ad5b1fa9612da40e738b1d99252c5ff2f"><div class="ttname"><a href="classpcl_1_1_feature.html#ad5b1fa9612da40e738b1d99252c5ff2f">pcl::Feature::compute</a></div><div class="ttdeci">void compute(PointCloudOut &amp;output)</div><div class="ttdoc">Base method for feature estimation for all points given in &lt;setInputCloud (), setIndices ()&gt; using th...</div><div class="ttdef"><b>Definition:</b> feature.hpp:189</div></div>
<div class="ttc" id="aclasspcl_1_1_kmeans_html"><div class="ttname"><a href="classpcl_1_1_kmeans.html">pcl::Kmeans</a></div><div class="ttdoc">K-means clustering.</div><div class="ttdef"><b>Definition:</b> kmeans.h:62</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html">pcl::NormalEstimation</a></div><div class="ttdoc">NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point....</div><div class="ttdef"><b>Definition:</b> normal_3d.h:200</div></div>
<div class="ttc" id="aclasspcl_1_1_normal_estimation_html_ac92dbbea9d923754b3d87d981a6bd131"><div class="ttname"><a href="classpcl_1_1_normal_estimation.html#ac92dbbea9d923754b3d87d981a6bd131">pcl::NormalEstimation::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> normal_3d.h:290</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a1952d7101f3942bac3b69ed55c1ca7ea"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">pcl::PCLBase::setInputCloud</a></div><div class="ttdeci">virtual void setInputCloud(const PointCloudConstPtr &amp;cloud)</div><div class="ttdoc">Provide a pointer to the input dataset</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:66</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html"><div class="ttname"><a href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a></div><div class="ttdoc">PointCloud represents the base class in PCL for storing collections of 3D points.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:173</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a2185a6453f8ad905d7bdf7b45754a160"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a2185a6453f8ad905d7bdf7b45754a160">pcl::PointCloud::width</a></div><div class="ttdeci">uint32_t width</div><div class="ttdoc">The point cloud width (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:413</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a3ca88d8ebf6f4f35acbc31cdfb38aa94"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a3ca88d8ebf6f4f35acbc31cdfb38aa94">pcl::PointCloud::is_dense</a></div><div class="ttdeci">bool is_dense</div><div class="ttdoc">True if no points are invalid (e.g., have NaN or Inf values).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:418</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_a4f34b45220c57f96607513ffad0d9582"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#a4f34b45220c57f96607513ffad0d9582">pcl::PointCloud::height</a></div><div class="ttdeci">uint32_t height</div><div class="ttdoc">The point cloud height (if organized as an image-structure).</div><div class="ttdef"><b>Definition:</b> point_cloud.h:415</div></div>
<div class="ttc" id="aclasspcl_1_1_point_cloud_html_af16a62638198313b9c093127c492c884"><div class="ttname"><a href="classpcl_1_1_point_cloud.html#af16a62638198313b9c093127c492c884">pcl::PointCloud::points</a></div><div class="ttdeci">std::vector&lt; PointT, Eigen::aligned_allocator&lt; PointT &gt; &gt; points</div><div class="ttdoc">The point data.</div><div class="ttdef"><b>Definition:</b> point_cloud.h:410</div></div>
<div class="ttc" id="aclasspcl_1_1_unary_classifier_html"><div class="ttname"><a href="classpcl_1_1_unary_classifier.html">pcl::UnaryClassifier</a></div><div class="ttdef"><b>Definition:</b> unary_classifier.h:61</div></div>
<div class="ttc" id="aclasspcl_1_1_unary_classifier_html_a25f5b661dacac2c2853a41e43a06cdea"><div class="ttname"><a href="classpcl_1_1_unary_classifier.html#a25f5b661dacac2c2853a41e43a06cdea">pcl::UnaryClassifier::setInputCloud</a></div><div class="ttdeci">void setInputCloud(typename pcl::PointCloud&lt; PointT &gt;::Ptr input_cloud)</div><div class="ttdoc">This method sets the input cloud.</div><div class="ttdef"><b>Definition:</b> unary_classifier.hpp:67</div></div>
<div class="ttc" id="aclasspcl_1_1_unary_classifier_html_aa17330bb95171dc661078966e77f25d4"><div class="ttname"><a href="classpcl_1_1_unary_classifier.html#aa17330bb95171dc661078966e77f25d4">pcl::UnaryClassifier::UnaryClassifier</a></div><div class="ttdeci">UnaryClassifier()</div><div class="ttdoc">Constructor that sets default values for member variables.</div><div class="ttdef"><b>Definition:</b> unary_classifier.hpp:50</div></div>
<div class="ttc" id="aclasspcl_1_1_unary_classifier_html_ac2bf8c9495c565cc2c637161d36ff064"><div class="ttname"><a href="classpcl_1_1_unary_classifier.html#ac2bf8c9495c565cc2c637161d36ff064">pcl::UnaryClassifier::~UnaryClassifier</a></div><div class="ttdeci">~UnaryClassifier()</div><div class="ttdoc">This destructor destroys the cloud...</div><div class="ttdef"><b>Definition:</b> unary_classifier.hpp:61</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="acommon_2include_2pcl_2common_2geometry_8h_html_a2fc89f0c26b7c7377fcd2851fa933b87"><div class="ttname"><a href="common_2include_2pcl_2common_2geometry_8h.html#a2fc89f0c26b7c7377fcd2851fa933b87">pcl::geometry::distance</a></div><div class="ttdeci">float distance(const PointT &amp;p1, const PointT &amp;p2)</div><div class="ttdef"><b>Definition:</b> geometry.h:60</div></div>
<div class="ttc" id="agroup__common_html_ga2bc4b9a4e25de1d0b00db4e41f0ad682"><div class="ttname"><a href="group__common.html#ga2bc4b9a4e25de1d0b00db4e41f0ad682">pcl::getFieldIndex</a></div><div class="ttdeci">int getFieldIndex(const pcl::PCLPointCloud2 &amp;cloud, const std::string &amp;field_name)</div><div class="ttdoc">Get the index of a specified field (i.e., dimension/channel)</div><div class="ttdef"><b>Definition:</b> io.h:59</div></div>
<div class="ttc" id="astructpcl_1_1_f_p_f_h_signature33_html"><div class="ttname"><a href="structpcl_1_1_f_p_f_h_signature33.html">pcl::FPFHSignature33</a></div><div class="ttdoc">A point structure representing the Fast Point Feature Histogram (FPFH).</div><div class="ttdef"><b>Definition:</b> point_types.hpp:1354</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z.html">pcl::PointXYZ</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates. (SSE friendly)</div><div class="ttdef"><b>Definition:</b> point_types.hpp:282</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_a_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_a.html">pcl::PointXYZRGBA</a></div><div class="ttdoc">A point structure representing Euclidean xyz coordinates, and the RGBA color.</div><div class="ttdef"><b>Definition:</b> point_types.hpp:540</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_r_g_b_l_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_r_g_b_l.html">pcl::PointXYZRGBL</a></div><div class="ttdef"><b>Definition:</b> point_types.hpp:638</div></div>
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